MSc, Ecology and Evolutionary Biology

Jyväskylä, Finland

University of Jyväskylä

2019 - 2016

• Thesis: “Variable light conditions affect warning signal efficacy in an aposematic polymorphic moth”
• Thesis grade: 4/5.
• My degree was graded as “Excellent”, the highest grade awarded to degrees at JYU
• Supervised by PhD Ossi Nokelainen and Prof. Johanna Mappes

BSc, Biological Sciences

Viçosa, Brazil

Federal University of Viçosa

2016 - 2009

• Monograph: “What drives changes in composition of ant assemblages along a vertical gradient?”
• Supervised by Profs. Fernando Augusto Schmidt and José Henrique Schoreder

Visiting student

Helsinki, Finland

University of Helsinki

2014 - 2013

• Attended Master’s level courses and did an internship at the research group “Social Evolution in Ants”

Master’s thesis development

Evolution of predator-prey interactions group

University of Jyväskylä

2019 - 2017

• I did my thesis work in Professor Johanna Mappes’s group. I investigated the effects of different light conditions to the predation pressure suffered by Arctia plantaginis, an aposematic species that is also color polymorphic. I attended the group’s weekly meetings during which we discussed papers, and presented research projects.

Research apprentice

Laboratório de Ecologia de Comunidades

Federal University of Viçosa

2016 - 2011

• I was part of different ant community ecology and biodiversity research projects, and conducted the project that resulted in my bachelor’s monograph. I had experience with sampling and identifying ants, data analysis and scientific writing, among other activities related to the research group.

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• I can analyze data using LMs, GLMs, GLMMs and conduct survival analyses. I can produce bar plots, line graphs, scatter plots, and histograms using R’s base graphics device and ggplot2. Finally, I know enough programming in R to clean and organize/sort data. I also know enough R to look for help online and implement it on my own provided that I have some time to work on it.

LaTeX

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• I can produce my own simple documents, including presentations and I know enough to implement different templates when necessary

Python

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• I can use libraries such as Pandas, Matplotlib and Seaborn to do basic data manipulation and plotting

Bash

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• I can write scripts to manipulate data and automate tasks

Computational and data literacy

London, UK

Natural History Museum

2018

• Duration: June 4 – June 8
• Course on reproducible data analysis and management, including data wrangling, Git and Github, R Markdown, and the basics of data analysis and visualisation using R.

Introduction to Shell for Data Science

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Datacamp

2019

• Course description: “The Unix command line has survived and thrived for almost 50 years because it lets people do complex things with just a few keystrokes. Sometimes called”the universal glue of programming," it helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds that may be halfway around the world. This course will introduce its key elements and show you how to use them efficiently.“

Writing Functions in R

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Datacamp

2019

• Course description: “Functions are a fundamental building block of the R language. You’ve probably used dozens (or even hundreds) of functions written by others, but in order to take your R game to the next level, you’ll need to learn to write your own functions. This course will teach you the fundamentals of writing functions in R so that, among other things, you can make your code more readable, avoid coding errors, and automate repetitive tasks. “

Introduction to Git for Data Science

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Datacamp

2019

• Course description: “Version control is one of the power tools of programming. It allows you to keep track of what you did when, undo any changes you decide you don’t want, and collaborate at scale with other people. This course will introduce you to Git, a modern version control tool that is very popular with data scientists and software developers, and show you how to use it to get more done in less time and with less pain. “

Intermediate Functional Programming with purrr

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Datacamp

2019

• Course description: “Have you ever been wondering what the purrr description (“A functional programming toolkit for R”) refers to? Then, you’ve come to the right place! This course will walk you through the functional programming part of purrr - in other words, you will learn how to take full advantage of the flexibility offered by the .f in map(.x, .f) to iterate other lists, vectors and data.frame with a robust, clean, and easy to maintain code. During this course, you will learn how to write your own mappers (or lambda functions), and how to use predicates and adverbs. Finally, this new knowledge will be applied to a use case, so that you’ll be able to see how you can use this newly acquired knowledge on a concrete example of a simple nested list, how to extract, keep or discard elements, how to compose functions to manipulate and parse results from this list, how to integrate purrr workflow inside other functions, how to avoid copy and pasting with purrr functional tools. “

Hierarchical and Mixed Effects Models in R

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Datacamp

2019

• Course description: “This course begins by reviewing slopes and intercepts in linear regressions before moving on to random-effects. You’ll learn what a random effect is and how to use one to model your data. Next, the course covers linear mixed-effect regressions. These powerful models will allow you to explore data with a more complicated structure than a standard linear regression. The course then teaches generalized linear mixed-effect regressions. Generalized linear mixed-effects models allow you to model more kinds of data, including binary responses and count data. Lastly, the course goes over repeated-measures analysis as a special case of mixed-effect modeling. This kind of data appears when subjects are followed over time and measurements are collected at intervals. Throughout the course you’ll work with real data to answer interesting questions using mixed-effects models. “

Reporting with R Markdown

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Datacamp

2019

• Course description: “Learn how to write a data report quickly and effectively with the R Markdown package, and share your results with your friends, colleagues or the rest of the world. Learn how you can author your own R Markdown reports, and how to automate the reporting process so that you have your own reproducible reports. By the end of the interactive data analysis reporting tutorial, you will be able to generate reports straight from your R code, documenting your work — and its results — as an HTML, pdf, slideshow or Microsoft Word document.“